In this paper, a new adaptive compression strategy based on the A* algorithm called SA* algorithm is proposed which combined the data compression with the heuristic method. A new evaluation function construction method is devised for the SA* algorithm, which is suitable for the frequency optimization of data compression. The simulation experiments are carried out to compare the adaptive compression algorithm based on heuristic search with the traditional equal-interval sample method applying Matlab platform, the experimental results show that the adaptive compression strategy based on A* algorithm can reduce the storage space of the data with lower distortion, and reach the balance of the quantity and quality of the data. This algorithm will be used to deal with the problem of large amount of historical load data in SCADA system, and it has the phenomenal ability to capture the tendency of changing load data, reducing the burden of the network and information processing system. The data cleaning and load forecasting time can be reduced as well.

/pdf/an-adaptive-compression-strategy-based-on-a-algorithm-4ijcmdlb3m.pdf

An Adaptive Compression Strategy Based on A* Algorithm

For the problem that mobile robot can not avoid large concave obstacles during navigation, this paper proposes a multi-state integrated navigation algorithm. The algorithm classifies the running state of mobile robot into running state, switching state and obstacle avoidance state according to different moving environment, and defines the state double switching conditions based on the running speed and running time of the mobile robot. The Artificial Potential Field Method (APFM) is used to navigate and observe the geometric configuration of adjacent obstacles in real time. When encountering an obstacle, the switching state is used to determine whether the state switching condition is satisfied, and the obstacle avoidance algorithm is executed to enter the obstacle avoidance state and enter the obstacle avoidance state to implement the obstacle avoidance algorithm. After the obstacle avoidance is completed, the state automatically switches back to the running state to continue the navigation task. The proposal of multi-state can solve the problem of local oscillation of traditional artificial potential field method in the process of avoiding large concave obstacles. Furthermore, the double-switching condition determination algorithm based on running speed and running time can realize smooth switching between states and optimize the path. The experimental results show that the algorithm can not only solve the local oscillation problem, but also reduce the obstacle avoidance time and improve the efficiency of the navigation algorithm.

Integrated Navigation Algorithm for Large Concave Obstacles

A Multi-path Routing Algorithm Base on A* Algorithm: A Multi-path Routing Algorithm Base on A* Algorithm

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A Multi-path Routing Algorithm Base on A* Algorithm: A Multi-path Routing Algorithm Base on A* Algorithm

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An Adaptive Compression Strategy Based on A* Algorithm

Integrated Navigation Algorithm for Large Concave Obstacles

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An Adaptive Compression Strategy Based on A* Algorithm

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